DETERMINAÇÃO DE AÇÚCARES SIMPLES, ÁCIDO MÁLICO E COMPOSTOS FENÓLICOS TOTAIS EM BAGAÇO DE MAÇÃ POR ESPECTROSCOPIA NO INFRAVERMELHO E MÉTODO DE CALIBRAÇÃO MULTIVARIADA

Abstract

The agro industry of the apple generates, during the processing, the pomace which is considered the main by-product. Studies show that of the total amount of fruit that is processed to obtain the apple juice, 20 to 40 correspond to this by-product, which, is usually destined as a complement in animal feed or delivered onto the soil as organic fertilizer. The chemical composition, in moist base, is constituted by moisture (80), fibers (5) and soluble solids (14) represented, mainly, by fructose, glucose and sucrose, as well as, by organic acids, represented, in the most part by malic acid. The apple pomace also presents phenolic compounds that are target nutrients of great sensory and nutritional importance. Therefore, the quantification of its constituents represents an important source of data in the characterization of apple pomace for biotechnological purposes, seeking to attribute an appropriate use for this by-product. The conventional methodologies used for the quantification of sugars, organic acids and total phenols, although being part of the routine analyses in the quality control laboratories, are onerous, time consuming and generate residues. The aim of this study was to develop a fast, versatile analytical technique, of low cost and no pollutant, using the diffuse reflectance infrared spectroscopy (DRIFTS) allied to methods of multivariate calibration (PLSR). For the construction of the multivariate models, the averages of the concentrations of the simple sugars, malic acid and total phenols were used, obtained by the conventional methodologies, as well as the data in the medium infrared spectroscopy (MID) and near (NIR), obtained in duplicate, and of the 52 spectra obtained for the samples of apple pomace, 47 made part of the set calibration and 5 of the set validation. The regression models for the prediction of the concentration of fructose, sucrose, total phenols and malic acid obtained better results in MID, with averages of relative standard errors of 3.9 (with 5 Latents Variable), 6.6 (with 5 Latents Variable), 6.4 (with 4 Latents Variable) and 5.9 (with 5 Latents Variable), respectively. Already the best capacity prediction for glucose concentration was obtained by NIR, in which the average of relative standard error was 7.4 (with 6 Latents Variable). The obtained results demonstrate the good capacity of prediction of the multivariate models based in infrared spectroscopy and characteristic advantages of the association DRIFTPLSR.